Input parameters in Chen and Abousleiman [37].

This paper proposes a physics-informed extreme learning machine (PIELM) for analyzing consolidation immediately after cavity expansion. The deep neural networks in traditional physics-informed neural network (PINN) framework are substituted by the extreme learning machine (ELM) network with only one hidden layer. By using exact definition of stress invarients, the distribution of excess water pressure after cavity expansion is rigorously incorporated into PIELM framework as initial conditions. Then, a loss vector is obtained by combining governing equation, initial conditions and boundary conditions, and the ELM network can be directly trained by optimising the loss vector via the least squares method. It is found that: (i) the PIELM approach can provide accurate prediction for consolidation analysis after cavity expansion; and (ii) the dissipation of excess water pressure heavily relies on its initial distribution that is related to soil mechanical behaviour. This proposed approach can serve as an efficient tool to interpret consolidation coefficient from piezocone penetration tests (CPTU) with measured data.

本文提出一种用于分析空腔扩张后即刻固结过程的物理信息极限学习机（physics-informed extreme learning machine, PIELM）。将传统物理信息神经网络（physics-informed neural network, PINN）框架中的深度神经网络，替换为仅含单个隐藏层的极限学习机（extreme learning machine, ELM）。借助应力不变量的精确定义，将空腔扩张后的超孔隙水压力分布作为初始条件严格融入PIELM框架。随后，通过耦合控制方程、初始条件与边界条件得到损失向量，并通过最小二乘法优化该损失向量，即可直接完成ELM网络的训练。研究结果表明：其一，PIELM方法可对空腔扩张后的固结分析提供精准预测；其二，超孔隙水压力的消散过程高度依赖其初始分布，而该分布与土体力学行为密切相关。该方法可作为一种高效工具，基于实测数据从孔压静力触探试验（piezocone penetration tests, CPTU）中反演固结系数。